Estimating Gaussian Curvatures from 3D Meshes

Jingliang Peng, Qing Li, C. C. Jay Kuo, Manli Zhou

Research output: Journal article publicationConference articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

A new approach to estimate the surface curvatures from 3D triangular mesh surfaces with Gaussian curvature's geometry interpretation is proposed in this work. Unlike previous work, the proposed method does not use local surface fitting, partial derivative computation, or oriented normal vector recovery. Instead, the Gaussian curvature is estimated at a vertex as the area of its small neighborhood under the Gaussian map divided by the area of that neighborhood. The proposed approach can handle vertices with the zero Gaussian curvature uniformly without localizing them as a separate process. The performance is further improved with the local Bezier curve approximation and subdivision. The effectiveness of the proposed approach for meshes with a large range of coarseness is demonstrated by experiments. The application of the proposed method to 3D surface segmentation and 3D mesh feature extraction is also discussed.

Original languageEnglish
Pages (from-to)270-280
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5007
DOIs
Publication statusPublished - 20 Nov 2003
Externally publishedYes
EventHuman Vision and Electronic Imaging VIII - Santa Clara, CA, United States
Duration: 21 Jan 200324 Jan 2003

Keywords

  • 3D feature extraction
  • 3D mesh
  • 3D surface segmentation
  • Differential geometry
  • Gaussian curvature

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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